With the fast development of artificial intelligence, robotics, and embedded system along with sensor technologies, the speed control mechanism is required in various other applications such as automatic or self-piloting aircraft, auto-driven vehicles, auto driven lifts and much other robotics based automation plants, etc. For each unpredictable and progressed vehicular framework accompanies a better route that is fit for utilizing the two GPS and INS related sign. There have been a noteworthy number of research works being completed towards creating sliding mode control framework. In case of inaccurate navigational data or no availability of navigational service, the cruise control could also stop working. Hence, there is a need to evolve up with a novel system offering reliable and fault tolerant navigation system in order to minimize the dependencies on GPS-based information and maximize the utilization of INS based information. This manuscript presents a dynamic cruise control system to achieve better navigation under uncertainties. The performance of the system is analyzed by incorporating sliding mode and fuzzy logic and achieves better accuracy in tracking error, computational complexity (28 sec of simulation time) under chattering and switching action operation.
Novel Framework for Navigation using Enhanced Fuzzy Approach with Sliding Mod...IJECEIAES
The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn’t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system.
Improving transport in Malta using GIS and LBSMatthew Pulis
A presentation prepared to the University of Malta as part of my MSc. Informatics. This seminar discusses ways and improvements how can a GIS driven system help and improve the current situation in Malta. This presentation also provides a survey discussing how the Maltese view the public transport and gives out interesting conclusions as to where the GIS needs to tackle. The study focuses mainly on ways as to where and how to improve the routes, promoting cultural places, buses ETA and taxi fleet handling amongst others.
COMPARATIVE STUDY ON VEHICLE DETECTION TECHNIQUES IN AERIAL SURVEILLANCEIJCI JOURNAL
Aerial surveillance system becomes a great trendy on past decades. Aerial surveillance vehicle tracking techniques plays a vital role and give rising to optimistic techniques continuously. This system can be very handy in various applications such as police, traffic monitoring, natural disaster and military. It is often covers large area and providing better perspective of moving objects. The detection of moving vehicle can be both from the dynamic aerial imagery, wide area motion imagery or images under low resolution and also the static in nature. It has been very difficult issue whether identify the object in the air view, the camera angles, movement objects and motionless object. This paper deals with comparative study on various vehicle detection and tracking approach in aerial videos with its experimental results and measures working condition, hit rate and false alarm rate
Accessibility Analysis and Modeling in Public Transport Networks - A Raster b...Beniamino Murgante
The document summarizes research on modeling accessibility in public transportation networks using a raster-based approach. The research aimed to create an accessibility indicator for jobs via public transit that had low data requirements to allow transfer to other regions. The study area was the capital region of Denmark. Accessibility was modeled using land use, transportation, and temporal components. The model calculated cost distances from population and job centers using rasterized transportation network data. Results showed variability in accessibility scores and generally aligned with commuting statistics. The raster approach allowed fast calculation with low data needs but did not fully account for travel time or mode changes.
The document describes a new method for travel time prediction using support vector machine (SVM) and weighted moving average (WMA). It uses historical traffic data classified into velocity classes using SVM. It then predicts travel time using a modified WMA equation applied to the SVM support vectors. This method is compared to previous methods like successive moving average, chain average, and artificial neural network. Experimental results show the proposed SVM and WMA method performs better in terms of accuracy and computational complexity.
Online Bus Arrival Time Prediction Using Hybrid Neural Network and Kalman fil...IJMER
This document presents a hybrid method for predicting bus arrival times using neural networks and Kalman filters. The proposed method combines a neural network trained on historical bus location and travel time data to make initial predictions, and then uses a Kalman filter to continuously update the predictions based on real-time GPS measurements from buses. The neural network model uses seven input nodes and a double hidden layer structure. The Kalman filter equations are used to fuse the neural network predictions with current GPS observations to improve prediction accuracy over time. A case study on a real bus route in Egypt showed the hybrid method achieved satisfactory prediction accuracy.
This document summarizes a research project that uses historical traffic data and artificial neural networks to predict estimated times of arrival for vehicles. The researchers collected GPS location and speed data along vehicle routes to develop ANN models. These models more accurately predicted travel times compared to averages or linear regression, with root mean square errors reduced from 72 to 34 seconds. While focused on speed and travel time, future work could integrate additional real-time factors like traffic levels and road conditions to improve predictions.
This study analyzed cycling routes in London using GPS data from the Strava application to understand the factors that influence cyclists' route choices. It found that cyclists' routes were on average 15.5-22.6% longer than the shortest path, suggesting cyclists prioritize route attributes like infrastructure, gradient, traffic levels and perceived safety over distance alone. The methodology could be improved by focusing on weekday data and using more realistic values when generating routes. Understanding route choice is important for designing infrastructure that encourages more cycling.
Novel Framework for Navigation using Enhanced Fuzzy Approach with Sliding Mod...IJECEIAES
The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn’t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system.
Improving transport in Malta using GIS and LBSMatthew Pulis
A presentation prepared to the University of Malta as part of my MSc. Informatics. This seminar discusses ways and improvements how can a GIS driven system help and improve the current situation in Malta. This presentation also provides a survey discussing how the Maltese view the public transport and gives out interesting conclusions as to where the GIS needs to tackle. The study focuses mainly on ways as to where and how to improve the routes, promoting cultural places, buses ETA and taxi fleet handling amongst others.
COMPARATIVE STUDY ON VEHICLE DETECTION TECHNIQUES IN AERIAL SURVEILLANCEIJCI JOURNAL
Aerial surveillance system becomes a great trendy on past decades. Aerial surveillance vehicle tracking techniques plays a vital role and give rising to optimistic techniques continuously. This system can be very handy in various applications such as police, traffic monitoring, natural disaster and military. It is often covers large area and providing better perspective of moving objects. The detection of moving vehicle can be both from the dynamic aerial imagery, wide area motion imagery or images under low resolution and also the static in nature. It has been very difficult issue whether identify the object in the air view, the camera angles, movement objects and motionless object. This paper deals with comparative study on various vehicle detection and tracking approach in aerial videos with its experimental results and measures working condition, hit rate and false alarm rate
Accessibility Analysis and Modeling in Public Transport Networks - A Raster b...Beniamino Murgante
The document summarizes research on modeling accessibility in public transportation networks using a raster-based approach. The research aimed to create an accessibility indicator for jobs via public transit that had low data requirements to allow transfer to other regions. The study area was the capital region of Denmark. Accessibility was modeled using land use, transportation, and temporal components. The model calculated cost distances from population and job centers using rasterized transportation network data. Results showed variability in accessibility scores and generally aligned with commuting statistics. The raster approach allowed fast calculation with low data needs but did not fully account for travel time or mode changes.
The document describes a new method for travel time prediction using support vector machine (SVM) and weighted moving average (WMA). It uses historical traffic data classified into velocity classes using SVM. It then predicts travel time using a modified WMA equation applied to the SVM support vectors. This method is compared to previous methods like successive moving average, chain average, and artificial neural network. Experimental results show the proposed SVM and WMA method performs better in terms of accuracy and computational complexity.
Online Bus Arrival Time Prediction Using Hybrid Neural Network and Kalman fil...IJMER
This document presents a hybrid method for predicting bus arrival times using neural networks and Kalman filters. The proposed method combines a neural network trained on historical bus location and travel time data to make initial predictions, and then uses a Kalman filter to continuously update the predictions based on real-time GPS measurements from buses. The neural network model uses seven input nodes and a double hidden layer structure. The Kalman filter equations are used to fuse the neural network predictions with current GPS observations to improve prediction accuracy over time. A case study on a real bus route in Egypt showed the hybrid method achieved satisfactory prediction accuracy.
This document summarizes a research project that uses historical traffic data and artificial neural networks to predict estimated times of arrival for vehicles. The researchers collected GPS location and speed data along vehicle routes to develop ANN models. These models more accurately predicted travel times compared to averages or linear regression, with root mean square errors reduced from 72 to 34 seconds. While focused on speed and travel time, future work could integrate additional real-time factors like traffic levels and road conditions to improve predictions.
This study analyzed cycling routes in London using GPS data from the Strava application to understand the factors that influence cyclists' route choices. It found that cyclists' routes were on average 15.5-22.6% longer than the shortest path, suggesting cyclists prioritize route attributes like infrastructure, gradient, traffic levels and perceived safety over distance alone. The methodology could be improved by focusing on weekday data and using more realistic values when generating routes. Understanding route choice is important for designing infrastructure that encourages more cycling.
"Detecting road lane is one of the key processes in vision based driving assistance system and autonomous vehicle system. The main purpose of the lane detection process is to estimate car position relative to the lane so that it can provide a warning to the driver if the car starts departing the lane. This process is useful not only to enhance safe driving but also in self driving car system. A novel approach to lane detection method using image processing techniques is presented in this research. The method minimizes the complexity of computation by the use of prior knowledge of color, intensity and the shape of the lane marks. By using prior knowledge, the detection process requires only two different analyses which are pixel intensity analysis and color component analysis. The method starts with searching a strong pair of edges along the horizontal line of road image. Once the strong edge is detected the process continues with color analysis on pixels that lie between the edges to check whether the pixels belong to a lane or not. The process is repeated for different positions of horizontal lines covering the road image. The method was successfully tested on selected 20 road images collected from internet. Ery M. Rizaldy | J. M. Nursherida | Abdul Rahim Sadiq Batcha ""Reduced Dimension Lane Detection Method"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19136.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/19136/reduced-dimension-lane-detection-method/ery-m-rizaldy"
Beyond good enough? Spatial Data Quality and OpenStreetMap dataMuki Haklay
State of the Map '09 presentation. Covering spatial data quality and comparison of Ordnance Survey data (Meridian 2, 10K Raster, MasterMap ITN) to OSM for England.
Some material appeared in previous presentation.
Performance of Phase Congruency and Linear Feature Extraction for Satellite I...IOSR Journals
This document summarizes research on extracting linear features from satellite images. It introduces using a phase congruency and linear feature extraction model combined with an adaptive smoothing algorithm. The paper aims to evaluate the advantages and limitations of this approach when applied to satellite image feature extraction. It also describes other common feature extraction methods, such as using mathematical morphology operations like dilation and erosion. Overall, the document reviews techniques for automated linear feature extraction from satellite imagery.
The document summarizes a proposed method for tracking road objects in highway videos. The method operates in two phases: 1) A spatial analysis phase that uses region descriptors matching to identify object interactions and states between consecutive frames. 2) A continuous temporal analysis phase that establishes object correspondences over time to generate trajectories. The method can detect, track, and count road objects accurately in videos with challenges like occlusions, state changes, and interactions between objects. It provides effective and stable tracking of road objects.
DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BA...civejjour
Land use and transportation planning are inter-dependent, as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING -- Part 1NanubalaDhruvan
In part 1 it is discussed about the introduction of traffic management and various methods and literature reviews of various papers and their specifications and finally the research gap
Engineering surveying, 5...ition w. schofieldrnkhan
An important book for knowledge of all types of engineering surveys
Engineering Surveying. Sixth Edition. W. Schofield. Former Principal Lecturer, Kingston University. M. Breach. Principal Lecturer, Nottingham Trent University.
Vibration based condition monitoring of rolling element bearing using xg boo...Conference Papers
This document summarizes a study on using vibration-based condition monitoring to detect faults in rolling element bearings. The study used the XGBoost machine learning algorithm and Orange data mining software to analyze vibration signals from bearings. Both statistical and image embedding methods were used to extract features from the signals. The image embedding approach improved classification accuracy compared to statistical features. Of the machine learning algorithms tested, XGBoost performed best, achieving a 91.25% classification accuracy using both feature extraction approaches on a bearing dataset from Case Western Reserve University.
Visual tracking using particle swarm optimizationcsandit
The problem of robust extraction of visual odometry from a sequence of images obtained by an
eye in hand camera configuration is addressed. A novel approach toward solving planar
template based tracking is proposed which performs a non-linear image alignment for
successful retrieval of camera transformations. In order to obtain global optimum a biometaheuristic
is used for optimization of similarity among the planar regions. The proposed
method is validated on image sequences with real as well as synthetic transformations and
found to be resilient to intensity variations. A comparative analysis of the various similarity
measures as well as various state-of-art methods reveal that the algorithm succeeds in tracking
the planar regions robustly and has good potential to be used in real applications.
Evaluation and Accuracy Assessment of Static GPS Technique in Monitoring of ...IJMER
It is well known that, deformation monitoring systems are considered, nowadays, to be the
back bone factor for human safety as well as preserving the ultimate economy of his achievements. In
this context, there has been always an increasing demand for precise deformation measurements in
keeping up several engineering structures and historical monuments. Measuring and monitoring
monumental deformation is the sequence of operations that allows the finding of movements of points
or features in a specified coordinate system, during two different times for the same investigated
feature. The time interval sometimes is the main factor in measuring horizontal deformation, especially
in loading test of steel bridges. Hence, the present paper investigates the accuracy of the GPS in
monitoring of horizontal deformation with respect to the time of observation. So, a practical
simulation test was made to assess the accuracy of GPS with time in measuring horizontal
deformation. The obtained results indicated that, the used methods and techniques presented in the
current research paper have possessed a very good accuracy, reliability and applicability in
monitoring horizontal deformations efficiently. The accuracy of measuring horizontal deformation of
points on structure using relative static technique of GPS is from (0.1mm) to (1.8mm) for time interval
from 30 minute to 5 minute and has R.M.S.E (0.3mm)
P ERFORMANCE M EASUREMENTS OF F EATURE T RACKING AND H ISTOGRAM BASED T ...ijcsit
In this paper, feature tracking based and histogram
based traffic congestion detection systems are
developed. Developed all system are designed to run
as real time application. In this work, ORB (Orien
ted
FAST and Rotated BRIEF) feature extraction method h
ave been used to develop feature tracking based
traffic congestion solution. ORB is a rotation inva
riant, fast and resistant to noise method and conta
ins the
power of FAST and BRIEF feature extraction methods.
Also, two different approaches, which are standard
deviation and weighed average, have been applied to
find out the congestion information by using
histogram of the image to develop histogram based t
raffic congestion solution. Both systems have been
tested on different weather conditions such as clou
dy, sunny and rainy to provide various illumination
at
both daytime and night. For all developed systems p
erformance results are examined to show the
advantages and drawbacks of these systems.
Examination of Ship Object Recognition in High Determination Sar Metaphors Ba...ijtsrd
In demand to make up the defects of some prevailing ship object recognition systems for high determination synthetic aperture radar SAR images, a ship object recognition system centered on information theory and Harris corner recognition for SAR images is anticipated in this paper. At the outset, the SAR appearance is pretreated, and later, it is alienated into super pixel squares by consuming the upgraded simple direct iterative bunching super pixel generation algorithm. Then, the self statistics rate of the super pixel squares is deliberate, and the threshold T1 is fixed to hand picked the aspirant super pixel squares. And formerly, the prolonged vicinity biased statistics entropy progression level threshold T2 is set to exclude the false alarm aspirant super pixel squares. As a final point, the Harris corner detection algorithm is used to route the recognition outcome and the quantity of the corner threshold T3 is set to riddle out the false alarm squares, and the ultimate SAR image object recognition outcome is attained. The efficiency and supremacy of the recommended algorithm are certified by equating the recommended method with the outcomes of constant false alarm rate CFAR recognition algorithm shared with morphological handling algorithm and further ship object recognition algorithms. Akshara Jayanthan | Dr. G. Karpagarajesh "Examination of Ship Object Recognition in High-Determination Sar Metaphors Based on Information Theory and Harris Corner Detection Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27972.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/27972/examination-of-ship-object-recognition-in-high-determination-sar-metaphors-based-on-information-theory-and-harris-corner-detection-technique/akshara-jayanthan
An In depth Review on Bridge Crack Detection Approachesijtsrd
Bridges are mega structures that have been utilized and built for millennia. These structures are highly effective in achieving transportation and commute between two highly inaccessible destinations easily. Bridges are also highly effective in reducing traffic was by allowing the use of an alternate path for the traffic flow to be resumed. This makes them highly versatile and extremely effective in various scenarios. But as with any constructed structures, these bridges need to be evaluated for their structural integrity and surveyed for any flaws or cracks that have been emerged over time. This is usually done manually by a civil engineer which is a time consuming process and can also introduce human error. Therefore to improve this procedure and number of related works have been analyzed extensively to achieve bridge crack detection through image processing methodologies. An effective approach has been envisioned through the use of convolutional neural networks and decision tree techniques to achieve bridge crack detection which will be further elaborated in the next edition of this research article. Ketan Ovhal | Ruturaj Lokhande | Omkar Kamble | Prathamesh Nanaware | Samarsingh Jadhav "An In-depth Review on Bridge Crack Detection Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42401.pdf Paper URL: https://www.ijtsrd.comengineering/computer-engineering/42401/an-indepth-review-on-bridge-crack-detection-approaches/ketan-ovhal
This document describes a MATLAB program called MamLand that was developed to easily construct a Mamdani fuzzy inference system for assessing landslide susceptibility. The program was used to create a landslide susceptibility map for Sinop, Turkey based on expert opinion and 7 conditioning factors. A landslide inventory of 351 locations was also obtained for the study area. The fuzzy inference system produced susceptibility degrees that were exported to a GIS and the resulting map had good accuracy according to statistical validation, demonstrating the potential of this expert opinion-based approach for landslide susceptibility mapping.
This document describes an improved particle filter tracking system that uses both color and moving edge information. It aims to address limitations of existing color-based particle filter tracking systems, such as inaccurate tracking when the target and background have similar colors, occlusion occurs, or the target is deformed. The proposed system selects an appropriate bounding box around the target using moving edge information to maintain an accurate target model during tracking. An experiment using 100 targets in 10 video clips showed the new system achieved a 94.6% accuracy rate for tracking, higher than an existing color-based particle filter system. It also had a 91.8% accuracy for occluded targets, much better than the previous system.
January 2021: Top Ten Cited Article in Computer Science, Engineering IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
A multi sensor-information_fusion_method_based_on_factor_graph_for_integrated...Ashish Sharma
The document proposes a multi-sensor information fusion method based on factor graph for integrated navigation systems. Key points:
- It constructs a factor graph framework where sensor measurements are factor nodes and navigation states are variable nodes, allowing for efficient fusion of asynchronous sensor data.
- The method formulates the optimal navigation solution as the maximum a posteriori estimate of the navigation state probability distribution based on all available sensor measurements.
- It is experimentally validated on two real-world datasets, demonstrating effectiveness compared to the widely used Federated Filter approach. Analysis of navigation with simulated data loss also verifies the method's ability to achieve sensor "plug and play" in software.
APPLICABILITY OF CROWD SOURCING TO DETERMINE THE BEST TRANSPORTATION METHOD B...IJDKP
Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper way to predict the traffic and recommend the best route considering the time factor and the people’s satisfaction on various transportation methods. Therefore, in this research using location awareness applications installed in mobile devices, data related to user mobility were collected by using crowdsourcing techniques and studied. Based on these observations an algorithm has been developed to overcome the problem. By using this, the best transportation method can be predicted as the results of the research. Therefore, people can choose what will be the best time slots & transportation methods when planning journeys. Throughout this research it has been proven that for the Sri Lankan context, the data mining concepts together with crowdsourcing can be applied to determine the best transportation method.
APPLICABILITY OF CROWD SOURCING TO DETERMINE THE BEST TRANSPORTATION METHOD B...IJDKP
Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper
way to predict the traffic and recommend the best route considering the time factor and the people’s
satisfaction on various transportation methods. Therefore, in this research using location awareness
applications installed in mobile devices, data related to user mobility were collected by using
crowdsourcing techniques and studied. Based on these observations an algorithm has been developed to
overcome the problem. By using this, the best transportation method can be predicted as the results of the
research. Therefore, people can choose what will be the best time slots & transportation methods when
planning journeys. Throughout this research it has been proven that for the Sri Lankan context, the data mining concepts together with crowdsourcing can be applied to determine the best transportation method.
This document presents a framework that uses a modified particle swarm optimization (PSO) algorithm for unmanned aerial vehicle (UAV) path planning to support wireless network user rate requirements. The framework formulates the problem to jointly optimize the path and energy consumption while satisfying the user's sum rate constraint. It first uses line of sight probability to find an optimal destination where the UAV can provide the required downlink rate to the user. Then, the modified PSO algorithm finds the most energy-efficient path from the source to that destination. Experiments show the framework provides an obstacle-avoiding 3D path, minimizes energy use and travel time, and improves user rate over other methods in different scenarios.
April 2024 - Top 10 Read Articles in Computer Networks & CommunicationsIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
"Detecting road lane is one of the key processes in vision based driving assistance system and autonomous vehicle system. The main purpose of the lane detection process is to estimate car position relative to the lane so that it can provide a warning to the driver if the car starts departing the lane. This process is useful not only to enhance safe driving but also in self driving car system. A novel approach to lane detection method using image processing techniques is presented in this research. The method minimizes the complexity of computation by the use of prior knowledge of color, intensity and the shape of the lane marks. By using prior knowledge, the detection process requires only two different analyses which are pixel intensity analysis and color component analysis. The method starts with searching a strong pair of edges along the horizontal line of road image. Once the strong edge is detected the process continues with color analysis on pixels that lie between the edges to check whether the pixels belong to a lane or not. The process is repeated for different positions of horizontal lines covering the road image. The method was successfully tested on selected 20 road images collected from internet. Ery M. Rizaldy | J. M. Nursherida | Abdul Rahim Sadiq Batcha ""Reduced Dimension Lane Detection Method"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19136.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/19136/reduced-dimension-lane-detection-method/ery-m-rizaldy"
Beyond good enough? Spatial Data Quality and OpenStreetMap dataMuki Haklay
State of the Map '09 presentation. Covering spatial data quality and comparison of Ordnance Survey data (Meridian 2, 10K Raster, MasterMap ITN) to OSM for England.
Some material appeared in previous presentation.
Performance of Phase Congruency and Linear Feature Extraction for Satellite I...IOSR Journals
This document summarizes research on extracting linear features from satellite images. It introduces using a phase congruency and linear feature extraction model combined with an adaptive smoothing algorithm. The paper aims to evaluate the advantages and limitations of this approach when applied to satellite image feature extraction. It also describes other common feature extraction methods, such as using mathematical morphology operations like dilation and erosion. Overall, the document reviews techniques for automated linear feature extraction from satellite imagery.
The document summarizes a proposed method for tracking road objects in highway videos. The method operates in two phases: 1) A spatial analysis phase that uses region descriptors matching to identify object interactions and states between consecutive frames. 2) A continuous temporal analysis phase that establishes object correspondences over time to generate trajectories. The method can detect, track, and count road objects accurately in videos with challenges like occlusions, state changes, and interactions between objects. It provides effective and stable tracking of road objects.
DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BA...civejjour
Land use and transportation planning are inter-dependent, as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING -- Part 1NanubalaDhruvan
In part 1 it is discussed about the introduction of traffic management and various methods and literature reviews of various papers and their specifications and finally the research gap
Engineering surveying, 5...ition w. schofieldrnkhan
An important book for knowledge of all types of engineering surveys
Engineering Surveying. Sixth Edition. W. Schofield. Former Principal Lecturer, Kingston University. M. Breach. Principal Lecturer, Nottingham Trent University.
Vibration based condition monitoring of rolling element bearing using xg boo...Conference Papers
This document summarizes a study on using vibration-based condition monitoring to detect faults in rolling element bearings. The study used the XGBoost machine learning algorithm and Orange data mining software to analyze vibration signals from bearings. Both statistical and image embedding methods were used to extract features from the signals. The image embedding approach improved classification accuracy compared to statistical features. Of the machine learning algorithms tested, XGBoost performed best, achieving a 91.25% classification accuracy using both feature extraction approaches on a bearing dataset from Case Western Reserve University.
Visual tracking using particle swarm optimizationcsandit
The problem of robust extraction of visual odometry from a sequence of images obtained by an
eye in hand camera configuration is addressed. A novel approach toward solving planar
template based tracking is proposed which performs a non-linear image alignment for
successful retrieval of camera transformations. In order to obtain global optimum a biometaheuristic
is used for optimization of similarity among the planar regions. The proposed
method is validated on image sequences with real as well as synthetic transformations and
found to be resilient to intensity variations. A comparative analysis of the various similarity
measures as well as various state-of-art methods reveal that the algorithm succeeds in tracking
the planar regions robustly and has good potential to be used in real applications.
Evaluation and Accuracy Assessment of Static GPS Technique in Monitoring of ...IJMER
It is well known that, deformation monitoring systems are considered, nowadays, to be the
back bone factor for human safety as well as preserving the ultimate economy of his achievements. In
this context, there has been always an increasing demand for precise deformation measurements in
keeping up several engineering structures and historical monuments. Measuring and monitoring
monumental deformation is the sequence of operations that allows the finding of movements of points
or features in a specified coordinate system, during two different times for the same investigated
feature. The time interval sometimes is the main factor in measuring horizontal deformation, especially
in loading test of steel bridges. Hence, the present paper investigates the accuracy of the GPS in
monitoring of horizontal deformation with respect to the time of observation. So, a practical
simulation test was made to assess the accuracy of GPS with time in measuring horizontal
deformation. The obtained results indicated that, the used methods and techniques presented in the
current research paper have possessed a very good accuracy, reliability and applicability in
monitoring horizontal deformations efficiently. The accuracy of measuring horizontal deformation of
points on structure using relative static technique of GPS is from (0.1mm) to (1.8mm) for time interval
from 30 minute to 5 minute and has R.M.S.E (0.3mm)
P ERFORMANCE M EASUREMENTS OF F EATURE T RACKING AND H ISTOGRAM BASED T ...ijcsit
In this paper, feature tracking based and histogram
based traffic congestion detection systems are
developed. Developed all system are designed to run
as real time application. In this work, ORB (Orien
ted
FAST and Rotated BRIEF) feature extraction method h
ave been used to develop feature tracking based
traffic congestion solution. ORB is a rotation inva
riant, fast and resistant to noise method and conta
ins the
power of FAST and BRIEF feature extraction methods.
Also, two different approaches, which are standard
deviation and weighed average, have been applied to
find out the congestion information by using
histogram of the image to develop histogram based t
raffic congestion solution. Both systems have been
tested on different weather conditions such as clou
dy, sunny and rainy to provide various illumination
at
both daytime and night. For all developed systems p
erformance results are examined to show the
advantages and drawbacks of these systems.
Examination of Ship Object Recognition in High Determination Sar Metaphors Ba...ijtsrd
In demand to make up the defects of some prevailing ship object recognition systems for high determination synthetic aperture radar SAR images, a ship object recognition system centered on information theory and Harris corner recognition for SAR images is anticipated in this paper. At the outset, the SAR appearance is pretreated, and later, it is alienated into super pixel squares by consuming the upgraded simple direct iterative bunching super pixel generation algorithm. Then, the self statistics rate of the super pixel squares is deliberate, and the threshold T1 is fixed to hand picked the aspirant super pixel squares. And formerly, the prolonged vicinity biased statistics entropy progression level threshold T2 is set to exclude the false alarm aspirant super pixel squares. As a final point, the Harris corner detection algorithm is used to route the recognition outcome and the quantity of the corner threshold T3 is set to riddle out the false alarm squares, and the ultimate SAR image object recognition outcome is attained. The efficiency and supremacy of the recommended algorithm are certified by equating the recommended method with the outcomes of constant false alarm rate CFAR recognition algorithm shared with morphological handling algorithm and further ship object recognition algorithms. Akshara Jayanthan | Dr. G. Karpagarajesh "Examination of Ship Object Recognition in High-Determination Sar Metaphors Based on Information Theory and Harris Corner Detection Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27972.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/27972/examination-of-ship-object-recognition-in-high-determination-sar-metaphors-based-on-information-theory-and-harris-corner-detection-technique/akshara-jayanthan
An In depth Review on Bridge Crack Detection Approachesijtsrd
Bridges are mega structures that have been utilized and built for millennia. These structures are highly effective in achieving transportation and commute between two highly inaccessible destinations easily. Bridges are also highly effective in reducing traffic was by allowing the use of an alternate path for the traffic flow to be resumed. This makes them highly versatile and extremely effective in various scenarios. But as with any constructed structures, these bridges need to be evaluated for their structural integrity and surveyed for any flaws or cracks that have been emerged over time. This is usually done manually by a civil engineer which is a time consuming process and can also introduce human error. Therefore to improve this procedure and number of related works have been analyzed extensively to achieve bridge crack detection through image processing methodologies. An effective approach has been envisioned through the use of convolutional neural networks and decision tree techniques to achieve bridge crack detection which will be further elaborated in the next edition of this research article. Ketan Ovhal | Ruturaj Lokhande | Omkar Kamble | Prathamesh Nanaware | Samarsingh Jadhav "An In-depth Review on Bridge Crack Detection Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42401.pdf Paper URL: https://www.ijtsrd.comengineering/computer-engineering/42401/an-indepth-review-on-bridge-crack-detection-approaches/ketan-ovhal
This document describes a MATLAB program called MamLand that was developed to easily construct a Mamdani fuzzy inference system for assessing landslide susceptibility. The program was used to create a landslide susceptibility map for Sinop, Turkey based on expert opinion and 7 conditioning factors. A landslide inventory of 351 locations was also obtained for the study area. The fuzzy inference system produced susceptibility degrees that were exported to a GIS and the resulting map had good accuracy according to statistical validation, demonstrating the potential of this expert opinion-based approach for landslide susceptibility mapping.
This document describes an improved particle filter tracking system that uses both color and moving edge information. It aims to address limitations of existing color-based particle filter tracking systems, such as inaccurate tracking when the target and background have similar colors, occlusion occurs, or the target is deformed. The proposed system selects an appropriate bounding box around the target using moving edge information to maintain an accurate target model during tracking. An experiment using 100 targets in 10 video clips showed the new system achieved a 94.6% accuracy rate for tracking, higher than an existing color-based particle filter system. It also had a 91.8% accuracy for occluded targets, much better than the previous system.
January 2021: Top Ten Cited Article in Computer Science, Engineering IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
A multi sensor-information_fusion_method_based_on_factor_graph_for_integrated...Ashish Sharma
The document proposes a multi-sensor information fusion method based on factor graph for integrated navigation systems. Key points:
- It constructs a factor graph framework where sensor measurements are factor nodes and navigation states are variable nodes, allowing for efficient fusion of asynchronous sensor data.
- The method formulates the optimal navigation solution as the maximum a posteriori estimate of the navigation state probability distribution based on all available sensor measurements.
- It is experimentally validated on two real-world datasets, demonstrating effectiveness compared to the widely used Federated Filter approach. Analysis of navigation with simulated data loss also verifies the method's ability to achieve sensor "plug and play" in software.
APPLICABILITY OF CROWD SOURCING TO DETERMINE THE BEST TRANSPORTATION METHOD B...IJDKP
Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper way to predict the traffic and recommend the best route considering the time factor and the people’s satisfaction on various transportation methods. Therefore, in this research using location awareness applications installed in mobile devices, data related to user mobility were collected by using crowdsourcing techniques and studied. Based on these observations an algorithm has been developed to overcome the problem. By using this, the best transportation method can be predicted as the results of the research. Therefore, people can choose what will be the best time slots & transportation methods when planning journeys. Throughout this research it has been proven that for the Sri Lankan context, the data mining concepts together with crowdsourcing can be applied to determine the best transportation method.
APPLICABILITY OF CROWD SOURCING TO DETERMINE THE BEST TRANSPORTATION METHOD B...IJDKP
Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper
way to predict the traffic and recommend the best route considering the time factor and the people’s
satisfaction on various transportation methods. Therefore, in this research using location awareness
applications installed in mobile devices, data related to user mobility were collected by using
crowdsourcing techniques and studied. Based on these observations an algorithm has been developed to
overcome the problem. By using this, the best transportation method can be predicted as the results of the
research. Therefore, people can choose what will be the best time slots & transportation methods when
planning journeys. Throughout this research it has been proven that for the Sri Lankan context, the data mining concepts together with crowdsourcing can be applied to determine the best transportation method.
This document presents a framework that uses a modified particle swarm optimization (PSO) algorithm for unmanned aerial vehicle (UAV) path planning to support wireless network user rate requirements. The framework formulates the problem to jointly optimize the path and energy consumption while satisfying the user's sum rate constraint. It first uses line of sight probability to find an optimal destination where the UAV can provide the required downlink rate to the user. Then, the modified PSO algorithm finds the most energy-efficient path from the source to that destination. Experiments show the framework provides an obstacle-avoiding 3D path, minimizes energy use and travel time, and improves user rate over other methods in different scenarios.
April 2024 - Top 10 Read Articles in Computer Networks & CommunicationsIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Ijde A Naïve Clustering Approach in Travel Time PredictionWaqas Tariq
Travel time prediction plays an important role in the research domain of Advanced Traveler Information Systems (ATIS). Clustering approach can be acted as one of the powerful tools to discover hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our proposed Naïve Clustering Approach (NCA), we partition a set of historical traffic data into several groups (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment, day group and time group. In each cluster, data objects are similar to one another and are sufficiently different from data objects of other groups. To choose centroid of a cluster, we introduce a new method namely, Cumulative Cloning Average. For experimental evaluation, comparison is also focused to the forecasting results of other four methods namely, Rule Based method, Naïve Bayesian Classification (NBC) method, Successive Moving Average (SMA) and Chain Average (CA) by using same set of historical travel time estimates. The results depict that the travel time for the study period can be predicted by the proposed strategy with the minimum Mean Absolute Relative Errors (MARE) and Mean Absolute Errors (MAE).
A Cooperative Localization Method based on V2I Communication and Distance Inf...IJCNCJournal
Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment. Vehicle positions obtained using V2I communication are more accurate because the known roadside unit (RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends more on the number of RSUs; however, the high installation cost limits the use of this approach. It also depends on nonlinear localization nature. They were neglected in several research papers. In these studies, the accumulated errors increased with time due to the linearity localization problem. In the present study, a cooperative localization method based on V2I communication and distance information in vehicular networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem, but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the proposed method has superior accuracy than existing methods, giving a root mean square error of approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in fault environments.
A COOPERATIVE LOCALIZATION METHOD BASED ON V2I COMMUNICATION AND DISTANCE INF...IJCNCJournal
Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment.
Vehicle positions obtained using V2I communication are more accurate because the known roadside unit
(RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends
more on the number of RSUs; however, the high installation cost limits the use of this approach. It also
depends on nonlinear localization nature. They were neglected in several research papers. In these studies,
the accumulated errors increased with time due to the linearity localization problem. In the present study,
a cooperative localization method based on V2I communication and distance information in vehicular
networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that
the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling
fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem,
but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed
method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the
proposed method has superior accuracy than existing methods, giving a root mean square error of
approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in
fault environments.
Energy-efficient data-aggregation for optimizing quality of service using mo...IJECEIAES
Quality of service (QoS) is essential for carrying out data transmission using resource-constrained sensor nodes in wireless sensor network (WSN). The introduction of mobile agent-based data aggregation is reported to offer energy efficiency; however, it has limitations, especially using a single mobile agent, where QoS optimization is not feasible. A review of existing studies showcases some dedicated attempts to use a mobile agent-based approach and address QoS enhancements. However, they were never combined studied. Therefore, this paper introduces a unique concept of retaining maximum QoS performance during data aggregation using a single mobile agent. The model introduces a unique communication framework, transmission provisioning using exceptional routine management, and simplified energy modeling. The proposed model has aimed for a lower delay and faster data aggregation speed with lower consumption of transmittance energy. The implementation and assessment of the model are carried out considering the challenging environment of WSN with multiple scales of data priority. The proposed model also contributes to evolving out with simplified communication vectors in a highly decentralized method. MATLAB's simulation outcome shows that the proposed system offers better delay performance, optimal energy management, and faster response time than existing schemes.
Traffic light control design approaches: a systematic literature reviewIJECEIAES
To assess different approaches to traffic light control design, a systematic literature review was conducted, covering publications from 2006 to 2020. The review’s aim was to gather and examine all studies that looked at road traffic and congestion issues. As well, it aims to extract and analyze protruding techniques from selected research articles in order to provide researchers and practitioners with recommendations and solutions. The research approach has placed a strong emphasis on planning, performing the analysis, and reporting the results. According to the results of the study, there has yet to be developed a specific design that senses road traffic and provides intelligent solutions. Dynamic time intervals, learning capability, emergency priority management, and intelligent functionality are all missing from the conventional design approach. While learning skills in the adaptive self-organization strategy were missed. Nonetheless, the vast majority of intelligent design approach papers lacked intelligent fear tires and learning abilities.
Traffic Management using IoT and Deep Learning Techniques: A Literature SurveyIRJET Journal
The document summarizes various literature on traffic management techniques using IoT and deep learning. It discusses object detection algorithms like YOLO, Faster R-CNN, and DeepSORT. It also reviews papers that use techniques like background subtraction, image processing, and ultrasonic sensors to detect and count vehicles and dynamically manage traffic light timing. Most studies aim to develop more accurate, real-time systems to reduce traffic congestion compared to traditional fixed-time traffic signals. They achieve improved results over previous methods in areas like mean average precision, tracking accuracy, and processing speed.
Minimizing routing overhead using signal strength in multi-hop wireless networkIJECEIAES
The document presents a novel routing technique called MROSS (Minimizing Routing Overhead using Signal Strength) for mobile ad-hoc networks. MROSS aims to jointly address problems of communication and network lifetime in mobile ad-hoc networks by reducing routing overhead and energy consumption compared to existing routing techniques. It introduces a unique network and communication model, with districts constructed from communication zones to form a new topology. The proposed technique selects communication links with the highest remaining battery to ensure routes have a lower probability of breaking.
This document discusses using GPS and PLC-HMI systems to control lavatory flush outlets on Indian trains. Specifically:
- GPS would detect when a train is near a station and send a signal to the PLC to close the lavatory outlet solenoid valve. When the train leaves the station, GPS would send a signal to open the valve.
- A PLC with 3 digital inputs and 2 digital outputs would automate opening and closing the solenoid valve that controls the lavatory outlet based on the GPS signals.
- Additional sensors like a level switch and motion detector are proposed to maintain cleanliness and ensure flush if the lavatory is used but not flushed.
Classification Approach for Big Data Driven Traffic Flow Prediction using Ap...IRJET Journal
This document discusses a proposed system for predicting traffic flow using big data and classification approaches. The system uses K-Nearest Neighbors (KNN) classification to identify traffic patterns and routes. It then uses a Convolutional Neural Network (CNN) to predict traffic flow levels on particular routes. The KNN identifies travel times between locations while the CNN predicts flow levels. The proposed system is evaluated using metrics like root mean squared error and mean relative error, and is found to improve accuracy and reduce prediction time compared to existing methods. The system aims to provide route recommendations to users based on minimum predicted traffic flow.
Vehicular ad hoc networks (VANETs) have seen tremendous growth in the last decade, providing a vast
range of applications in both military and civilian activities. The temporary connectivity in the vehicles can also
increase the driver’s capability on the road. However, such applications require heavy data packets to be shared on
the same spectrum without the requirement of excessive radios. Thus, e-client approaches are required which can
provide improved data dissemination along with the better quality of services to allow heavy traffic to be easily
shared between the vehicles. In this paper, an e-client data dissemination approach is proposed which not only
improves the vehicle to vehicle connectivity but also improves the QoS between the source and the destination. The
proposed approach is analyzed and compared with the existing state-of-the-art approaches. The effectiveness of the
proposed approach is demonstrated in terms of the significant gains attained in the parameters namely, end to end
delay, packet delivery ratio, route acquisition time, throughput, and message dissemination rate in comparison with
the existing approaches.
A computer vision-based lane detection technique using gradient threshold and...IJECEIAES
Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.
Abstract - Positioning is a fundamental component of human life to make meaningful interpretations of the environment. Without knowledge of position, human beings are like machines and have very limited capabilities to interact with the environment. Even machines in today’s world can be made smarter if positioning information is made available to them. Indoor positioning of pedestrians is the broad area considered in this thesis. A foot mounted pedestrian tracking device has been studied for this purpose. Systems which utilize foot mounted inertial navigation system has been in the literature for more than two decades. However very few real time implementations have been possible. The purpose of this thesis is to benchmark and improve the performance of one such implementation.
A novel k-means powered algorithm for an efficient clustering in vehicular ad...IJECEIAES
Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.
Spatio-Temporal Data Analysis using Deep LearningIRJET Journal
This document provides a literature review of spatio-temporal data analysis using deep learning techniques. It discusses applications in domains such as transportation, social media, and environmental issues. For transportation, examples of deep learning models for traffic forecasting and accident detection are discussed. For social media, models for event detection from social media data and sentiment analysis of disaster tweets are described. For environmental issues, applications discussed include wind and rainfall prediction, land use classification from satellite imagery, and crop yield modeling from aerial imagery. The document provides an overview of different deep learning techniques used for spatio-temporal data analysis across these application domains.
The document provides information about the Global Positioning System (GPS). It begins with an introduction to GPS and its use in vehicle systems. It then discusses the history and development of GPS. The document outlines some current issues with GPS, including the need for a unified vision, improvements to support military effectiveness, and lack of balance between GPS segments. It also discusses GPS III satellites. The document describes the system architecture of a GPS instrumented vehicle and the effects of radio frequency interference on GPS signals. It concludes by discussing GPS monitoring networks operated by the Department of Defense and civilian organizations.
Similar to A dynamic cruise control system for effective navigation system (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
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Agile Methodology: Before Agile – Waterfall, Agile Development.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
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A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
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INS and GPS for developing up with a superior type of navigational framework. Use of such joint navigational
framework can be seen in various types of vehicle proceeding onward land [9], water [10], and air [11].
There are additionally different types of weapons like rockets uses such joint navigational framework [12].
It is observed that there has been the great number of research work completed in this respects, and yet, it can't
be overlooked that current research methodologies were not appeared to effectively benchmark or professed to
offer unrivalled navigational framework [13]. Existing specialists are additionally endeavoring to build
cost-effective INS-GPS navigational framework where the conspicuous test is to correctly introduce the outside
edge inferable from a corrupted working procedure of gyros or comparable types of different sensors. One of
the powerful instruments to address this issue could be by utilizing a sliding mode controller (SMC) [14],
which is the core of each development control framework. For each unpredictable and progressed vehicular
framework accompanies a better route that is fit for utilizing the two GPS and INS related sign. The recent
research of [15] has provided the idea of SMC concept for a robot manipulator.
There has been a noteworthy number of research work being completed towards creating sliding
mode control framework; be that as it may, the lion's share of them are related with certain inalienable
issues of managing dynamic framework. It was seen that current methodologies don't provide food up to
the vulnerability requests of the dynamic framework, particularly in transmission arrange. With dominant
part of the work lacking benchmarking and exclusion of considering the contextual analysis of taking care of
the navigational issue, the SMC has turned out to be one of the less investigated arrangements
toward upgrading route framework utilizing GPS and INS. Therefore, this manuscript presents a dynamic
cruise control system (DCCS) to achieve better navigation performance and computational complexity.
The manuscript is categorized with different sections like review of literature in Section 2, problem statement
in Section 3, proposed system in Section 4, results and analysis in Section 5, and conclusion in Section 6.
2. BACKGROUND
Many research works are presented by concentrating on planning a powerful navigational
framework utilizing INS just as GPS, with extraordinary strategies and mechanism. Existing works are
progressively centered on to achieve better accuracy, where the investigation driven by Dacheng et al. [16]
demonstrated upgraded affectability by limiting Doppler frequency when the navigational arrangement of
INS is coordinated with GPS. Reception system of channels is one extraordinary methodology in this
examination course. As indicated by Cho et al. [17], an upgraded Kalman channel would improve
the navigational exhibition, particularly concentrating on cost-adequacy organization perspective.
Arrangement of comparative Kalman channel of broadened type was found in introduced by Duong and
Nguyen [18] for a compelling estimation of physical navigational parameters for example frame of mind,
speed, area, and so forth. Utilization of Kalman channel was likewise found in work examined by Fang and
Gong [19] where the prime goal was to consolidate a prescient plan of an incorporated navigational
administration. The investigation was tried over constant sensors mounted on an airplane to find that offers
better exactness. There was additionally research work which says that creating a navigational framework for
a submerged vehicle is extremely a difficult one. Such research work has been done by Lee et al. [20], which
is intended for limiting the blunders in navigational framework, particularly with the GPS information.
The work did by Li and Sun [21] has additionally utilized Kalman channel of broadened structure for
improving the precision related to navigational information. Introduced by Wu et al. [22] have concentrated
on building up a prescient calculation utilizing a Kalman channel. The examination likewise details
a commotion model for remunerating the sign. Yan et al. [23] have additionally done work utilizing
Kalman channel.
Contextual analysis of the flying article and its improvement of route information was examined by
Nakanishi et al. [24]. The researcher has tended to the issue related to unwavering quality of information
from GPS by executing a unified system of sensor combination with a guide of updates got in the offbeat
method of correspondence. Contextual investigation of the route of the land vehicle has been considered in
introduced by Qin et al. [25], where the researcher has utilized Kalman channel just as standard fuzzy
inference framework for limiting blunders in route framework. Another researcher, viz. Sun et al. [26] have
completed a test based examination where a GPS INS based route framework. It was additionally discovered
that coupling among the navigational parameters noteworthy influence the identification of sign. This issue
has been explored by Jamal [27] by correcting the mistakes related to detecting during fast information catch.
As indicated by the researcher, tight coupling outcomes are intending to these issues. Use of the neural
system was seen in introduced by Jaradat and Hafez [28] where a relapse based methodology was executed
for tending to the correspondence delay in route framework. The existing framework has likewise seen
examination towards recognizing and tending to flaws in the navigational framework. The work did by
Xin et al. [28] has utilized measurable based methodology for deficiency distinguishing proof. Also there are
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different examinations towards improving navigational execution, yet a proficient controller can be obtained
utilizing a sliding mode configuration approach. There have been different research-based methodologies
towards SMC [29, 30] towards tending to various periods of research-based issues. Elgammal and
El-naggar [31] has utilized this methodology for creating dynamic power channel. The comparative course of
work has been found in introduced by Fei et al. [32], which observed selection of neural system just as
the fuzzy-based sliding methodology was utilized for limiting the gabbing impact. Concentrate towards cost
advancement utilizing fuzzy based controller configuration has been displayed by Li et al. [33] where
the researcher has ordinarily used interim sort two fuzzy methodologies for tending to postpone in
the framework. The exhibition of controller configuration utilizing fuzzy based sliding window was
demonstrated to be upgraded considering the contextual investigation of vehicle and driving issues. A work
of Shen et al. [34] has utilized a versatile component of building up a controller framework utilizing the same
fuzzy put together methodology concerning the highest point of diagram hypothesis. The business locales
the issues related with the multi-operator framework. The joining of adaptiveness is likewise found in
introduced by Wang and Fei [35] where the disposal of jabbering has been done alongside a versatile element
of dependability. Wen et al. [36, 37] have built up a component where the dynamic highlights have been
consolidated just as they have likewise centered on creating shortcoming tolerant actuator structure with
enough versatile highlights. Another exceptional type of usage has been seen in introduced by Yu et al. [38]
where a bio-motivated calculation has been intended for better physical control framework. The work did by
Zhao et al. [39] has planned a fuzzy controller framework considering the contextual investigation of shut
circle framework related to the multi-input and multi-yield framework. Also, other enhancement based
methodologies were discussed with SMC plan, for example, Someswari and Anil Kumar Tiwari [40, 41].
Thus, there have been different files of research works, where the fuzzy-based framework has been observed
to be effectively utilized in upgrading the presentation of sliding mode converter and also a number of work
being performed towards navigational methodologies too. The next section discusses research problems
associated with the existing system.
3. PROBLEM IDENTIFICATION
At present, the modern vehicles are equipped with a cruise control system, which is one reduces
the effort of manually maintaining the acceleration while driving. However, there is no physical connection
between the cruise control device and navigation system. However, with the inclusion of more smart and
intelligent components on-vehicle navigation system, it is imperative that the cruise control system could be
further integrated with the navigational system. At present, there is not a single research work toward this
issue. However, there is a good possibility as there is certain research discussion on using TCP for cruise
control. In case of inaccurate navigational data or no availability of navigational service, the cruise control
could also stop working. Therefore, the problem statement for the proposed system can be stated as “it is
quite challenging to meet as an effective cruise control system must be able to perform proper navigation by
understanding the static and dynamic attributes associated with the existing navigational system”. The next
section briefs about the proposed research methodologies used to address the above set of problems.
4. THE PROPOSED METHODOLOGY
The prime motive of proposed novel system is to evolve up offering reliable and fault tolerant
navigation system in order to minimize the dependencies on GPS-based information and maximize
the utilization of INS based information which has been not much considered in the exsiitngsystem [42-45].
In order to enhance the performance of the navigational system, the introduced study incorporats
the variables of the mathematical model with the planar motion of the vehicle. Also, it has been aimed to
improvise the fuzzy controller using sliding mode (proposed in [46]) and achieve accurate direction of
mobility considering the vehicular characteristics as a constraint. In this model, the fuzzy logic is associated
with the formulation of the various rule set to have a better dependency on resources during the navigational
process. Also, it has been observed that not many types of research were observed with optimization in
the fuzzy processing using SMC. Also, the use of conventional training/learning based algorithm can enhance
the dependency of the dataset and is never feasible in online analysis. Also, it has been observed that some of
the recent researches have used fuzzy logic type-2 and SMC to address the various issues of the control
system. It is found that fuzzy logic type-2 has good potential to handle uncertainty problems but are always
associated with computational complexity problems. Thus, the following model is introduced to fulfill
the needs of performance enhancement in terms of accuracy and computational efficiency. The design of
the proposed system is carried out using analytical research methodology with the following scheme,
as shown in Figure 1.
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Variables of
Mathematical
Model
(control input
(n), scalar
parameter (λ))
Core Variables
Planar motion of the
vehicle
Destination
Static
Motion
Nonlinearity/Uncertainity
develop a Cruise control
surface (S)
Apply Control law
Dynamics of sliding motion
Switching
action
Chattering
Smoothing
/boundary
layer
Fuzzy
processor
Precise
Readings
of
Navigation
Performance Analysis
(Tracking error, Computational Complexity, Torque)
Figure 1. Architecture of the proposed study
4.1. Model for working performance enhancement
The proposed study is intended to design a planar motion of the vehicle with the targeted
destination. In practical, the destination is always considered to be static (e.g., home, office, park, hospital,
etc.); however, there may be a possibility that such destination could also be mobile (e.g., tracking another
vehicle). Therefore, the target destination can static or mobile and the system uses INS in this part to feed
the input of vehicle speed and the directionality vector of destination. For simpler implementation, both
vehicle and (mobile) destination have considered approximately equal speed. This incorporates the most
challenging scenario in navigation system implemented till date. Mathematical modeling is carried out for
formulating the vehicle-destination geometry along with consideration of various forms of uncertainty
problems surfaced from readings of INS navigational system. The study develops a model which relates
to vehicle and destination and extracts various uncertain information which is fed to fuzzy logic and
sliding mode control for obtaining accurate navigational information. The proposed system considers various
issues in the navigation system (chattering) and incorporated the switching control and smoothing (boundary
layer) mechanism.
4.2. Model for computational efficiency
This part of the study applies an analytical modeling approach to perform optimization.
The complete optimization is based on the concept that SMC offers better compensation plan for mitigating
uncertainty problems along with fuzzy logic. However, while doing so, it cannot address the problem if
the uncertainty attributes are of fluctuating origin, and in such case, SMC is no longer productive when
working with the fuzzy logic system. Therefore, one of the better solutions is to offer optimization. In this
case, the surface design of sliding mode is introduced by re-considering the proposed vehicle navigational
system that is essentially MEMS-based. The first part of the optimization is meant with determining
the parameter of the surface, followed by implying control law by considering state trajectories. Simple
mathematical modeling will be carried out towards exploiting the dynamics of sliding motion in order to
incorporate more flexibility in design parameters. Another proof of optimization will be investigated in this
research stage by minimizing the number of Ruleset to a considerably lower degree. The proposed system
incorporates the design of the sliding surface, control law, and computational complexity.
4.3. System model
The proposed study has considered that at some condition, the precise system modeling is not
possible as the external aspects like frictions, forces affect the stability of the system. As it is observed that
there are practical difficulties into modelling a cruise navigation system accurately because of various
influencing factors of forces acting a surface including friction and all, which has got significant influence on
the stability of the control system. These kinds of highly dynamic and nonlinear systems required a flexible
and adaptive dynamic cruise control system. To achieve this fuzzy control mechanism based on sliding mode
is adopted.
For system design, the mathematical approach is considered where if the DCCS comes with
the dynamics' and control input ‘n’ for the control then the system can be defined as:
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4649
(𝑥 𝑡⁄ ) = 𝑚(𝑥, 𝑡) + 𝜂(𝑡)
The proposed DCCS is assumed as of 2nd
order and is normalizes as 𝑥⃗ = 𝑚 + 𝑛. In order to design a control
mechanism for displacement and speed. The dynamic cruise control surface (S) for the DCCS is designed as:
𝑆 = (
𝑑
𝑑𝑡
+ 𝜆) 𝑥̃ + 𝜆𝑥⃗
where 𝜆 is meant with a single scalar parameter which has the main impact on the cruise control surface (S).
The change in S at S=0, and 𝑥̅ = 𝑚 + 𝑛 is resolved as:
𝑆⃗ = 0
𝑥⃗ − 𝑥̅ 𝑑 + 𝜆𝑥̃ = ()
𝑚 + 𝑛 − 𝑥⃗ 𝑑 + 𝜆𝑥⃗ = 0
𝑛 = −𝑚 + 𝑥⃗𝑑 − 𝜆𝑥⃗
The value of the scalar parameter (𝜆) with a positive parameter is almost arbitrary, and it reduces
the dynamics of the system during sliding.
The DCCS model (m) is an approximation of the DCCS model. The approximated control law can
be normalized as 𝑛̂ = −𝑚̂ + 𝑥̂𝑑 − 𝜆𝑥̂. Figure 2 shows a geometrical view of the DCCS if it is slide on
the sliding surface with slope only 𝜃.
In order to maintain the working of the system in a sliding surface, a switching action is introduced.
The switching action (𝐴 𝑠𝑤) can be defined as:
𝐴 𝑠𝑤= − 𝐴×𝑠𝑖𝑔𝑛(𝑆)
where A is a large positive constant, and its value is selected if 𝐴 = {
−𝐴 @ 𝑆 > 0
𝐴 @ 𝑆 < 0
. This switching control is
discontinuous across the cruise control surface (S) =0.
During steady state, the variable "A" commute at higher frequency lies between 𝐴 = 𝐴 and 𝐴 = −𝐴.
The control law is considered between these low and high-frequency control. As a result, the discontinuous
high-frequency control of switching action is appropriate in the navigation system, but it leads to chattering
mechanism in control switching. The chattering is the harmful mechanism which leads to low control
accuracy and high heat losses in the power circuits. Figure 3 indicates the Chattering phenomenon in
the control system.
In order to solve the chattering problem, smoothing is performed over a chattering phenomenon
where a smooth approximation (Sap) is replaced in place of the discontinuous sign. The plot in Figure 4
indicates the smoothing in the chattering phenomenon, i.e.:
𝐴 𝑠𝑤= − 𝐴×𝑆 𝑎𝑝(𝑆)
𝑆 𝑎𝑝 ==
{
−1 @ 𝑆
𝜙⁄ < 0
0 @ 𝑆
𝜙⁄ < 1
1 @ 𝑆
𝜙⁄ < −1
where 𝜙 indicates the thickness of the boundary layer.
The mathematical model of DCCS is derived from the fuzzy-based sliding mode by addressing
the above-stated problems in the dynamic modelling. Figure 5 gives the 2-link system of controlling arm
having robot links 1 and 2. The model includes displacements 𝜃1 and 𝜃2 with the length of links 𝑑1 and 𝑑2.
The mass of every link is represented as 𝑀1 and 𝑀2. During this mathematical modelling, Lagrange-Euler
formation is used with the following dynamic equation.
𝑀(𝜙)𝜙̈ 𝑑 + 𝐹(𝜙, 𝜙̈)𝜙̈ 𝑑 + 𝐺(𝜙) = 𝜏
[
𝑁11 𝑁12
𝑁12 𝑁22
] × [
𝜙̈1
𝜙̈2
] + [
−𝐹12 𝜙̈2 −𝐹12(𝜙̈1 + 𝜙̈2)
𝐹12 𝜙̈1 0
] × [
𝜙̈1
𝜙̈2
] + [
𝐺1 𝑔
𝐺2 𝑔
] = [
𝐴1
𝐴2
]
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where,
𝑁11 = (𝑀 + 𝑀2) × 𝑑1
2
+ 𝑀2 × 𝑑2
2
+ 𝑀2 × 𝑑1 × 𝑑2 × cos 𝜙2
𝑁12 = 𝑀2 × 𝑑2
2
+ 𝑀2 × 𝑑1 × 𝑑2 × cos 𝜙2
𝑁22 = 𝑀2 × 𝑑2
2
𝐹12 = 𝑀2 × 𝑑1 × 𝑑2 × sin 𝜙2
𝑔1 = (𝑀 + 𝑀2) × 𝑑1 × cos 𝜙2 + 𝑀2 × 𝑑2 × cos(𝜙1 + 𝜙2)
𝑔2 = 𝑀2 × 𝑑2 × cos(𝜙1 + 𝜙2)
Thus, it can be said that the proposed fuzzy SMC has isolates the unknown parameters in
the navigation system. The system has adopted a control law for both low frequency and high-frequency
control. In this DCCS system, the robustness is parameterized by considering the fast varying closed loops
while the chattering in the system is minimized by using boundary layer (smoothing) technique. The next
section highlights the results and analysis of the proposed DCCS mechanism.
Figure 2. Graphical representation of
sliding surface
Figure 3. Graphical representation of chattering
in control switching
Figure 4. Smoothing in chattering phenomenon Figure 5. Graphical representation of chattering
in control switching
5. RESULTS AND ANALYSIS
The system design of the proposed DCCS model is performed by using MATLAB based simulation.
The system considers the fuzzy input for cruise control. The performance of the system is analyzed in terms of
accuracy in identifying the error and computational complexity. Figure 6 gives the error analysis in DCCS
model in terms of tracking error (in radians) Vs. time (in seconds) for both the (a) link 1 and (b) link 2.
The simulation is conducted over the DCCS system for 10 sec of time (x-axis). The mass of the links 1, 2 is
adjusted as 0.75 kg and 1.25 kg respectively while the scalar parameter (𝜆) is adjusted to 5 for both the links.
Further, the boundary layer thickness is adjusted to 0.02. The outcomes are given in Figure 6 suggests that
the tracking of errors in the navigation system is gradually increasing and becomes error free as the time goes in
tracking the path. Hence, it can be observed that the in 10 seconds of time, the tracking error becomes zero.
The torque in the navigation system accuracy of the DCCS model. In Figure 7, the torque at link-1
where it can be observed that torque (is represented in the y-axis) against time (in x-axis). Here, the torque
variation is intially leading to with better accuracy in navigation and decrement in torque rate is observed due
chattering effect. In the navigation system, the speed of the cruise is controlled by the torque. Similarly,
Figure 8 indicates the torque variations at link-2 where the torque is intially leading with better accuracy in
navigation and in later part the switching action is taking place in the navigation system.
7. Int J Elec & Comp Eng ISSN: 2088-8708
A dynamic cruise control system for effective navigation system (T. Someswari)
4651
Figure 6. Tracking error analysis at (a) link-1 and (b) link-2
Figure 7. Torque analysis at link-1
Figure 8. Torque analysis at link-2
In the proposed DCCS model, the boundary layer concept is used to control the navigation system
behaviour between link-1 and link-2, where the energy in this boundary layer is linked to the sliding surface
of the cruise. This dynamic nature of the proposed system brings effective handling of the speed of the cruise.
In the proposed system, the robustness is measure with the reduced chattering in the navigation system,
and this is achieved through boundary layer. Further, the computational complexity analysis is performed by
considering the time of computation, the clock speed of the navigation system. From the simulation, it is
observed that the simulation phase considered only 28 sec and a clock speed of 1801 MHz.
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4652
6. CONCLUSION
This manuscript presents a DCCS to achieve better navigation under uncertainties. The proposed
system incorporates the fuzzy logic and sliding mode technique along with control law. The performance of
the system is analyzed by incorporating sliding mode and fuzzy logic and achieves better accuracy in
tracking error, computational complexity (28 sec of simulation time) under chattering and switching action
operation. Thus, the proposed system can handle the dynamic disturbances in the navigation system, and it
can handle the current navigation issues in GPS and INS. Further, the proposed system can be incorporated
with other machine learning approaches to enhance the performance of the navigation.
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BIOGRAPHIES OF AUTHORS
T. Someswari working as assistant professor, EEEdept at The Oxford college of Engineering
has published 3 interantional journal papers. I am pursuing Ph.D in VTU under the guidance of
Dr. Anilkumar and Dr. Nagaraj R Ihave completed my B.E. degree in 2007 from JNTU,
Hydearbad in Electrical and Electroincs and M.E. from JNTU, Anantapur in 2010.
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Dr. Anilkumar Tiwari has total of 26 years of experience in Industry, research and teaching
(UG & PG) students. I have been worked as Professor & HOD, Department of ECE in
The Oxford College of Engineering, Bangalore Jun 2011. Served as Senior PG faculty in Air
Force Technical College, Bangalore Jun 2009–May 2011. Served in Indian Air Force as Senior
Engineer super specialising in the areas of Microwave and Radar Engg and Satellite & Digital
Communication, Advanced Missile Guidance and Control Research assignment at Defence
Institute of advanced Technology and Defence services staff college.Presently working as
Director, Amity School of Engineering.
Dr. Nagaraj R has completed his Ph.D degree from vtu and has published various international
and national journals.He has taken his Masters degree from Gulbarga university and B.E from
Bangalore university.He has worked as a Director, Dhirubai institute of sciences and The Oxford
college of engineering. Presently, He is working as a vice chancellor, Kalasalingam University,
krishnankoil, Madurai, India